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Digitization in Health Care and the Journey to Become Data-Driven

Published on 8/6/2018
Health care delivery organizations compete to provide services in a constantly shifting industry. The top and bottom lines of the income statement are constantly assaulted by industry pressures like decreases in reimbursements, shifting business models, increased regulations and competitive forces from traditional organizations and new entrants. Feeling those pressures, health care administrators are focusing their attention on strategies like cost containment, consumerism and vertical integration. Given significant investment in digitization over the past decades, CIOs are increasingly being asked to shift away from making the foundational investment and focus on obtaining the return from that investment.  

There is no doubt the information in health care delivery systems in the U.S. and increasingly across the globe is being digitized. Yet while the ones and zeroes are in place, much of the health care delivery system is still seeking the value that they expected with digitalization. Why? One reason, perhaps, is that we have not applied the capabilities that come with digitization to processes and services or products in health care. Once we do, it seems reasonable health care will see the increased efficiency, consumer-centricity and more profitable business models that have benefited industries like airlines and banking. In a data-driven industry like health care, it starts with how digitized data becomes value-added information.  

Turning digitized data into value-added information sounds simple enough, but a walk through the HIMSS solution gallery would suggest different. The complexities of health care data, government incentives and technology advancements have given rise to a relatively new sector of the health care IT industry: analytics and intelligence. Hundreds (if not thousands) of companies are vying to help health care organizations become data-driven through artificial intelligence, machine learning and digital transformation. However, it can be difficult to pinpoint exactly what the power of these tools may mean for the industry. (Even Gartner’s definition of analytics is over 130 words.) 

The journey to becoming a data-driven health care organization 

I tend to think about the journey to becoming data-driven in simpler terms: as questions that inform the strategies needed to get the answers.

Question #1: What happened, and why did it happen?

The Spanish philosopher George Santayana wrote, “Those who cannot remember the past are condemned to repeat it.” Describing what has occurred and diagnosing how it happened has been the work of informaticists, process engineers and data analysts for a long time. Reactive data is used to summarize history and inform shifts in clinical, financial and operational processes. Technology advancements in data warehouses (or, if you prefer, “data lakes”) have increased diagnostic capabilities as data from multiple sources can be normalized, combined and summarized, providing incremental value and informing new insights. Optimization of processes based on history, both the good times and the rough times, is becoming easier with data accessibility and modern visualization tools.

Question #2: What will happen next? 

“The consequences of our actions are always so complicated, so diverse, that predicting the future is a very difficult business indeed.” That wisdom comes to us through the voice of Albus Dumbledore, the headmaster of the wizarding school Hogwarts in the Harry Potter series. The complexities and consequences of the actions of patients and care providers certainly make predicting the future as difficult, if not a form of wizardry. 

That said, digitized data and the democratization of statistical analysis tools are allowing data scientists and analysts to identify trends across healthcare and the factors that either show causation or correlation with those trends. In so doing, we are increasingly able to predict the future based on historical data. This is especially true in health system operations, as the nexus of clinical, operational, situational and environmental data sets is leading to increased accuracy in the prediction of patient volumes, transitions of care and staffing needs. The predictive algorithms developed today are trained against real data sets with known outcomes. Once deployed, advancements in machine learning capabilities are allowing algorithms to increase in accuracy based on outcomes and results with limited to no human intervention.  

Question #3: What should I do about it? 

There are a lot of definitions of artificial intelligence (AI), which has led the concept to become a buzzword for companies providing data-driven solutions. When further investigated, however, many of those claiming to provide AI are really offering simple algorithms requiring human interventions (sometimes AI is actually not that artificial and of marginal intelligence). 

The definition of AI that I prefer is perhaps the simplest: It is “the capability of a machine to imitate intelligent human behavior.” In other words, the computer is analyzing as much data as needed, simulating an outcome and identifying the optimal path, ideally in near real-time. Still, in health care, imitating human behavior might not be good enough. 

In 1950, medical knowledge doubled every 50 years. By 1980, it took 7 years for medical knowledge to double. It is estimated that by 2020, the doubling time will drop to 73 days. Global knowledge is rapidly outpacing the individual’s ability to assimilate and apply it in a timeframe that should be acceptable for patient care. AI applied to medical knowledge may expand the opportunities for physicians by linking the most recent research to the specific patient situation and recommending pathways and treatment options. I look forward to the day when AI becomes more than a marketing buzzword in health care: The opportunities for advancing patient care, access and quality are limitless.  

Question #4: How do I optimize for the future?  

Analytics should inform organizational strategic decision making. The key is to build accurate assumptions in models that exist somewhere in the confluence between the outcomes of current daily decisions and the way future investments will affect those outcomes. In other words: One must simulate reality as much as possible to predict the future. 

The tools used for simulation run the gamut between Microsoft Excel and expensive proprietary software. The tool is less important than the validity of the assumptions and the accurate understanding of how multiple variables work together to form an outcome. Simulation advancements combined with increased processing power are fueling AI’s ability to aid in decision making at the point-of-care delivery or in the moment decisions need to be made, as well as in the longer term, informing strategic decision making.

Key considerations for health care leaders who value actionable data

The application of these types of questions to problem statements could begin to build the near-and-next technology and process strategies for organizations as they seek to become data-driven. There are a couple of more important decisions to consider, however, as the journey to become data-driven is complex.

Consideration #1: The delivery vehicle for information matters

Should key information be delivered in a report that is auto-generated and sent, on a large screen that is always on, inside of workflow applications or via a text? The person receiving the information and the context in which they receive it matters. 

A data strategy for a centralized process only works if the work to centralize that process has been done. By contrast if an organization is trying to drive federated decision making based on a set of the rules, it is better to consider delivery of that information inside of near or future-state workflows so as not to add burden to already busy staff.  

Consideration #2: The form information comes in is important to drive decisions

We all know how we like to receive information used in our daily work. What if, all of a sudden, that information doubled or tripled and was based on machine-driven intelligence?  Information overload, relevance, and targeted trend analysis must be considered as we think through smart alerting and visualization.

Consideration #3: Relevant and accurate insights come from clean data

The inputs to analytical tools and algorithms must be “clean” for the algorithms to gain new insights. It’s important that any organization on this journey has a strategy to ensure the data is validated for accuracy prior to incorporating advanced analytics in the workflow.

Leveraging AI and Intelligence to optimize health care outcomes 

On the surface, it may seem like AI, machine learning and predictive intelligence are a distant future state in health care. However, right now, there is a tremendous amount of investment going into how information will be leveraged to increase care coordination, the quality of care, cost optimization and consumer-focused strategies. 

Perhaps the nearest term, least controversial opportunity for predictive analytics and AI in health care is to drive decision making aimed at optimizing care access and throughput across the continuum of care. In the U.S., like many countries, many have felt there is capacity in today’s health care infrastructure to ensure timely access for every member of the population. Daily decisions based on decision support tools and AI have the potential to improve throughput through optimization of care venues, transitions of care, discharge and turn-around times. If the capacity is in the system, perhaps more intelligent workflows are the key to unlocking that capacity.  

It’s an exciting time in health care: It feels like we are on the verge of an explosion of creativity and invention. The opportunity is there to increase quality, efficiency and access. We’ve worked so hard to digitize the data – what we do with it will be what defines our future.

We’re focused on optimizing the patient’s journey through the health system, including resource utilization, from the time they enter the hospital through the discharge and subsequent placement across the continuum. Learn more here.